Population biology: concepts and models
Population biology: concepts and models
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Computer vision based methods for detecting weeds in lawns
Machine Vision and Applications
Spatial and spectral methods for weed detection and localization
EURASIP Journal on Applied Signal Processing
Wavelet transform to discriminate between crop and weed in perspective agronomic images
Computers and Electronics in Agriculture
Towards machine vision based site-specific weed management in cereals
Computers and Electronics in Agriculture
Automatic detection of crop rows in maize fields with high weeds pressure
Expert Systems with Applications: An International Journal
Automatic expert system based on images for accuracy crop row detection in maize fields
Expert Systems with Applications: An International Journal
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We present a robust and automatic method for evaluating the accuracy of Crop/Weed discrimination algorithms. The proposed method is based on simulated agronomic images and a Crop/inter-row Weed discrimination algorithm can be divided into the two following steps. Firstly a crop row detection (Hough transform) is performed from the identification of the crop line vanishing point taking the opportunity of the perspective geometry of the scene. Afterwards, the discrimination between crop and weeds is done by a region-based segmentation method using a blob-colouring analysis and an inter-row Weed Infestation Rate (WIR) can be estimated. We propose to test and validate the robustness of this method on simulated images with perspective. To simulate photos taken from a virtual camera, a pinhole camera model is used and the field is modelled according to the spatial periodicity distribution of crop seedlings and the spatial distribution of weed species based on stochastic processes (Poisson process, Neyman-Scott aggregative process or a mixture of both). For each simulated image, the comparison between the initial inter-row WIR and the detected inter-row WIR informs us about the errors made by the algorithm. A pixel classification between the two classes - Crop and Weed - is performed in order to identify misclassification errors. This comparison demonstrates an accuracy of better than 85% is possible for inter-row weed detection.